A bundling-ideation technique that mines Amazon's own "frequently bought together" module on top competitor listings, combined with first-principles reasoning about what a buyer of the core product naturally uses alongside it.
Amazon's frequently-bought-together data is itself a market signal of real co-purchase behavior around a product. Cross-referencing that data across several top listings in a niche, plus asking what the target customer's broader routine looks like, generates candidate bundle components.
Before finalizing a product, pull up frequently-bought-together on multiple top competitor listings and list the complementary items that recur; combine with reasoning about the customer's use context to decide what to bundle. Bundling raises average order value and differentiates a listing from single-item competitors, feeding into Differentiation vs. Race-to-the-Bottom.
Brand Analytics' Market Basket Analysis (the same "frequently bought together" data) has two uses beyond bundling ideation: reverse-engineering keywords from co-purchased products, and mimicking the PDP presentation of complementary products that shoppers already buy alongside yours.
The same 'frequently purchased together' data (surfaced in Brand Analytics as Market Basket Analysis) has a second use beyond bundling ideation: since a complementary product's buyers likely share your customer avatar, its listing is a source of both keywords and page structure worth copying.
Apply: Reverse-engineer the listing keywords of products frequently bought with yours and add relevant ones to your own listing; also mimic aspects of their PDP layout, since a shared customer avatar means what converts for them likely converts for you too.
Из тем: Product Research & Validation